Predictive Analysis: The Knowledge of the Future

It is said that every generation has its language of literacy; if you had knowledge of that, then you were considered literate of that age; the dark ages when we lived a life of war, people who had good knowledge of it became generals and kings. When the industrial age rolled in, people who understood machines built up industries. How accurate can predictive analysis be?

Today we live in an age where the language of literacy is technology. And people who understand technology rate good quality data very highly, which has resulted in it becoming one of the essential assets. Now the question arises: what do people do with data? What if I told you that they see the future with it? 

What is predictive analysis?

Predictive analysis is a methodology that uses data to predict future events or behaviors. This kind of analysis studies past scenarios and patterns to identify connections between them and learn about their outcomes. With this data, researchers can predict outcomes observed before in similar behavior patterns and manipulate scenarios to obtain the desired results.

Predictive analytics, as the word suggests, forecasts future unknown events. The objective is to go beyond knowing what has happened in order to make a better judgment of what will happen in the future. It makes use of machine learning, statistics, data modeling, and mining techniques to analyze the past and estimate the future. 

Predictive Analytics decision-making arm for many industries. It dominates the industries like Advertising, Marketing, Finance, E-commerce, Insurance, Manufacturing, Retail, Government sectors, Oil & Gas, Education, and so on. 

How does predictive analysis work?

 Predictive analytics originates from statistical science and, at its nucleus, involves giving the presence of particular variables in a large dataset a certain result. This result is used to compute the probability of a certain event occurring in the future.

There are two main statistical modeling approaches used in predictive analytics: Classification models and regression models.

1. Classification models

The classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. For. e.g. It  will tell you if a member is likely to either stay with the company or leave within a timeline, based on certain criteria. 

2. Regression models

Regression models will predict an actual number which will use ongoing data as opposed to binary data. For example, a logistic regression could be used to evaluate how the odds of a patient having a heart attack (binary variable) change with every additional BMI value (continuous variable).

Predictive analysis Pros

  • Frauds

    • Predictive Analytics is a blessing to cybersecurity. They can detect frauds, threats etc using these techniques.
  • Optimization

    • Predictive analytics helps in identifying likes and dislikes of the customers and thereby recognise buying patterns and optimizing the marketing strategies.
  • Decision making

    • Granting loans, accepting insurance claims etc can be done based on the data models used in predictive analytics.
  • Operations

    • E-commerce industries can make decisions on inventory management. Oil & gas industries can predict equipment maintenance plans based on predictive analytics.

Predictive analysis Cons

  • Human factors

    • Researchers claim that Predictive Analytics models/algorithms fail to consider emotions, moods, relationships etc when anticipating the patterns.
  • Time

    • The Predictive Analytics models have to be revised over time. People change over time. A model applicable at one point in time may not be useful later on.
  • Cost

    • It is costly to implement Predictive analytics in terms of resources, tools and time.
  • Privacy & Security

    • Predictive Analytics deals with data. Storing such a large amount of data is a huge challenge. The data might also contain personal information of the users, etc which needs to be protected.

A deeper look into predictive analysis

Have you ever thought that your phone is listening to all your conversations? You probably have at least one friend who believes in that conspiracy because they spoke about something or even just thought about it. Then they ended up getting an advertisement for the exact product or at least something related to it.

A lot of other industries are largely using predictive analysis. It helps physicians make accurate diagnoses or determine the outcome of treatments for people with specific conditions. This has also helped reduce emergency room wait times by up to 15 percent.

It has helped the retail market by correctly predicting with retail audit which stock would be sold more and thus which should be stocked up more. Predictive analysis has even made big leaps in other domains like banking, manufacturing, public transportation, and cyber security, to name a few.

Now, this doesn’t mean that everything is sunshine and rainbows, and it will solve world hunger. There have been a lot of cases recently about how far collection of data targets people like you and me. Companies have infiltrated into our personal lives, which has resulted in lawsuits against companies like Facebook and Cambridge Analytica.

You might think, what is the worst thing that these people could be doing? well, think about this: if you have a really good friend who you have known for a long time and who you hang out with regularly, it will be very easy for you to predict what he would do in certain situations.

To have an idea of this, you would have spent a lot of time together and shared a hell of experiences as well, so how does predictive analysis have the power to do the same without even knowing who you are?

Well, companies like Cambridge Analytica have 5000 data points to define who you are, what you are likely to do, and what you are likely to buy. The data they upload onto that is purchased from companies like Facebook and Google, who run under cover of making money through advertisement. In reality, we, the consumers of those technologies, are the product.

There is always something volatile that humanity creates where people are completely divided if this is good for us or will be the death of us all. I know this sounds like painting a really bad picture on a simple tool designed to predict client partners to serve them better, but the main concern here lies with the fact about how the data is collected for that tool to actually function.

Would you feel comfortable knowing that a third-party company knows all your movements and choices? That you are being turned into a puppet to who the next company is trying to sell their next new shiny product? So what does all of this boil down to?

A simple fact is that this is like fire, we can learn how to control it and advance as a civilization and learn to eat cooked food and socialize, or we can use it to become an advanced civilization that gives birth to a self-learning AI that ends up ruling the world and enslaving the humanity. What is it going to be?

Predictive analytics & QuestionPro

QuestionPro provides analytics as part of the Surveys product, which helps gain insights into the past and make decisions for the future. There are various features like reports, statistics packages, data filtering, cross-tabulation, trend analysis, text analysis, etc., which can help customers in predictive decision-making!

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Authors: Shubhada and Jackson / Fahad Ahmed Shaikh